Object-relational mappers (ORMs)

An object-relational mapper (ORM) is a code library that automates the
transfer of data stored in relational databases tables into objects that
are more commonly used in application code.

Why are ORMs useful?

ORMs provide a high-level abstraction upon a
relational database that allows a developer to write
Python code instead of SQL to create, read, update and delete data and
schemas in their database. Developers can use the programming language they
are comfortable with to work with a database instead of writing SQL
statements or stored procedures.

For example, without an ORM a developer would write the following SQL
statement to retrieve every row in the USERS table where the
zip_code column is 94107:

SELECT * FROM USERS WHERE zip_code=94107;

The equivalent Django ORM query would instead look like
the following Python code:

The ability to write Python code instead of SQL can speed up web application
development, especially at the beginning of a project. The potential
development speed boost comes from not having to switch from Python code
into writing declarative paradigm SQL statements. While some software
developers may not mind switching back and forth between languages, it's
typically easier to knock out a prototype or start a web application using
a single programming language.

ORMs also make it theoretically possible to switch an application between
various relational databases. For example, a developer could use
SQLite for
local development and MySQL in production. A production
application could be switched from MySQL to PostgreSQL with
minimal code modifications.

In practice however, it's best to use the same database for local development
as is used in production. Otherwise unexpected errors could hit in production
that were not seen in a local development environment. Also, it's rare that
a project would switch from one database in production to another one unless
there was a pressing reason.

Do I have to use an ORM for my web application?

Python ORM libraries are not required for accessing relational
databases. In fact, the low-level access is typically provided by another
library called a database connector, such as
psycopg (for PostgreSQL)
or MySQL-python (for
MySQL). Take a look at the table below which shows how ORMs can work with
different web frameworks and connectors and relational databases.

The above table shows for example that SQLAlchemy can work with varying
web frameworks and database connectors. Developers can also use ORMs without
a web framework, such as when creating a data analysis tool or a batch
script without a user interface.

What are the downsides of using an ORM?

There are numerous downsides of ORMs, including

Impedance mismatch

Potential for reduced performance

Shifting complexity from the database into the application code

Impedance mismatch

The phrase "impedance mismatch" is commonly used in conjunction with ORMs.
Impedance mismatch is a catch-all term for the difficulties that occur when
moving data between relational tables and application objects. The gist
is that the way a developer uses objects is different from how data is
stored and joined in relational tables.

Potential for reduced performance

One of the concerns that's associated with any higher-level abstraction or
framework is potential for reduced performance. With ORMs, the performance
hit comes from the translation of application code into a corresponding SQL
statement which may not be tuned properly.

ORMs are also often easy to try but difficult to master. For example, a
beginner using Django might not know about the
select_related() function
and how it can improve some queries' foreign key relationship performance.
There are dozens of performance tips and tricks for every ORM. It's possible
that investing time in learning those quirks may be better spent just
learning SQL and how to write stored procedures.

There's a lot of hand-waving "may or may not" and "potential for" in this
section. In large projects ORMs are good enough for roughly 80-90% of use
cases but in 10-20% of a project's database interactions there can be
major performance improvements by having a knowledgeable database
administrator write tuned SQL statements to replace the ORM's generated
SQL code.

Shifting complexity from the database into the app code

The code for working with an application's data has to live somewhere. Before
ORMs were common, database stored procedures were used to encapsulate the
database logic. With an ORM, the data manipulation code instead lives within
the application's Python codebase. The addition of data handling logic in the
codebase generally isn't an issue with a sound application design, but it does
increase the total amount of Python code instead of splitting code between
the application and the database stored procedures.

Django's ORM

Django's ORM works well
for simple and medium-complexity database operations. However, there are often
complaints that the ORM makes complex queries much more complicated than
writing straight SQL or using SQLAlchemy.

It is technically possible to drop down to SQL but it ties the queries to a
specific database implementation. The ORM is coupled closely with Django so
replacing the default ORM with SQLAlchemy is currently a hack workaround.
Note though it is possible that swappable ORM backends will be possible
in the future as it is now possible to change the
template engine for rendering output in Django.

Since the majority of Django projects are tied to the default ORM, it is
best to read up on advanced use cases and tools for doing your best work
within the existing framework.

SQLAlchemy ORM

SQLAlchemy is a well-regarded
Python ORM because it gets the abstraction level "just right" and
seems to make complex database queries easier to write than the Django
ORM in most cases. There is an entire page on SQLAlchemy
that you should read if you want to learn more about using the library.

Pony

SQLObject ORM

Schema migrations

Schema migrations, for example when you need to add a new column to an
existing table in your database, are not technically part of ORMs. However,
since ORMs typically lead to a hands-off approach to the database (at the
developers peril in many cases), libraries to perform schema migrations
often go hand-in-hand with Python ORM usage on web application projects.

Database schema migrations are a complex topic and deserve their own page.
For now, we'll lump schema migration resources under ORM links below.

General ORM resources

This example GitHub project
implements the same Flask application with several different ORMs:
SQLAlchemy, Peewee, MongoEngine, stdnet and PonyORM.

Martin Fowler addresses the
ORM hate
in an essay about how ORMs are often misused but that they do provide
benefits to developers.

The Rise and Fall of Object Relational Mapping
is a talk on the history of ORMs that doesn't shy away from some
controversy. Overall I found the critique of conceptual ideas worth
the time it took to read the presentation slides and companion text.

If you're confused about the difference between a connector, such as
MySQL-python and an ORM like SQLAlchemy, read this
StackOverflow answer
on the topic.

What ORMs have taught me: just learn SQL
is another angle in the ORM versus embedded SQL / stored procedures debate.
The author's conclusion is that while working with ORMs such as SQLAlchemy
and Hibernate (a Java-based ORM) can save time up front there are issues
as a project evolves such as partial objects and schema redundancies.
I think the author makes some valid points that some ORMs can be a shaky
foundation for extremely complicated database-backed applications. However,
I disagree with the overriding conclusion to eschew ORMs in favor of
stored procedures. Stored procedures have their own issues and there are
no perfect solutions, but I personally prefer using an ORM at the start
of almost every project even if it later needs to be replaced with
direct SQL queries.

The Vietnam of Computer Science
provides the perspective from Ted Neward, the originator of the phrase
"Object/relational mapping is the Vietnam of Computer Science" that he
first spoke about in 2004. The gist of the argument against ORMs is
captured in Ted's quote that an ORM "represents a quagmire which starts
well, gets more complicated as time passes, and before long entraps its
users in a commitment that has no clear demarcation point, no clear
win conditions, and no clear exit strategy." There are follow up posts on
Coding Horror
and another one from Ted entitled
thoughts on Vietnam commentary.

Turning the Tables: How to Get Along with your Object-Relational Mapper
coins the funny but insightful phrase "database denial" to describe how
some ORMs provide a usage model that can cause more issues than they
solve over straight SQL queries. The post then goes into much more detail
about the problems that can arise and how to mitigate or avoid them.

SQLAlchemy and Peewee resources

A comprehensive list of SQLAlchemy and
Peewee ORM resources can be found on their respective
pages.